Abstract

Background

The past decade has seen an abundance of transcriptional profiling studies of preclinical
models of persistent pain, predominantly employing microarray technology. In this
study we directly compare exon microarrays to RNA-seq and investigate the ability
of both platforms to detect differentially expressed genes following nerve injury
using the L5 spinal nerve transection model of neuropathic pain. We also investigate
the effects of increasing RNA-seq sequencing depth. Finally we take advantage of the
“agnostic” approach of RNA-seq to discover areas of expression outside of annotated
exons that show marked changes in expression following nerve injury.

Results

RNA-seq and microarrays largely agree in terms of the genes called as differentially
expressed. However, RNA-seq is able to interrogate a much larger proportion of the
genome. It can also detect a greater number of differentially expressed genes than
microarrays, across a wider range of fold changes and is able to assign a larger range
of expression values to the genes it measures. The number of differentially expressed
genes detected increases with sequencing depth. RNA-seq also allows the discovery
of a number of genes displaying unusual and interesting patterns of non-exonic expression
following nerve injury, an effect that cannot be detected using microarrays.

Conclusion

We recommend the use of RNA-seq for future high-throughput transcriptomic experiments
in pain studies. RNA-seq allowed the identification of a larger number of putative
candidate pain genes than microarrays and can also detect a wider range of expression
values in a neuropathic pain model. In addition, RNA-seq can interrogate the whole
genome regardless of prior annotations, being able to detect transcription from areas
of the genome not currently annotated as exons. Some of these areas are differentially
expressed following nerve injury, and may represent novel genes or isoforms. We also
recommend the use of a high sequencing depth in order to detect differential expression
for genes with low levels of expression.